Overview

Dataset statistics

Number of variables11
Number of observations569
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.0 KiB
Average record size in memory88.2 B

Variable types

Numeric10
Categorical1

Alerts

mean radius is highly correlated with mean perimeter and 4 other fieldsHigh correlation
mean perimeter is highly correlated with mean radius and 5 other fieldsHigh correlation
mean area is highly correlated with mean radius and 4 other fieldsHigh correlation
mean smoothness is highly correlated with mean compactness and 4 other fieldsHigh correlation
mean compactness is highly correlated with mean perimeter and 5 other fieldsHigh correlation
mean concavity is highly correlated with mean radius and 6 other fieldsHigh correlation
mean concave points is highly correlated with mean radius and 6 other fieldsHigh correlation
mean symmetry is highly correlated with mean smoothness and 1 other fieldsHigh correlation
mean fractal dimension is highly correlated with mean smoothnessHigh correlation
y is highly correlated with mean radius and 5 other fieldsHigh correlation
mean radius is highly correlated with mean perimeter and 5 other fieldsHigh correlation
mean perimeter is highly correlated with mean radius and 5 other fieldsHigh correlation
mean area is highly correlated with mean radius and 4 other fieldsHigh correlation
mean smoothness is highly correlated with mean compactness and 4 other fieldsHigh correlation
mean compactness is highly correlated with mean radius and 7 other fieldsHigh correlation
mean concavity is highly correlated with mean radius and 7 other fieldsHigh correlation
mean concave points is highly correlated with mean radius and 6 other fieldsHigh correlation
mean symmetry is highly correlated with mean smoothness and 2 other fieldsHigh correlation
mean fractal dimension is highly correlated with mean smoothness and 1 other fieldsHigh correlation
y is highly correlated with mean radius and 5 other fieldsHigh correlation
mean radius is highly correlated with mean perimeter and 3 other fieldsHigh correlation
mean perimeter is highly correlated with mean radius and 3 other fieldsHigh correlation
mean area is highly correlated with mean radius and 3 other fieldsHigh correlation
mean compactness is highly correlated with mean concavity and 1 other fieldsHigh correlation
mean concavity is highly correlated with mean compactness and 2 other fieldsHigh correlation
mean concave points is highly correlated with mean radius and 5 other fieldsHigh correlation
y is highly correlated with mean radius and 4 other fieldsHigh correlation
mean radius is highly correlated with mean perimeter and 5 other fieldsHigh correlation
mean texture is highly correlated with yHigh correlation
mean perimeter is highly correlated with mean radius and 5 other fieldsHigh correlation
mean area is highly correlated with mean radius and 5 other fieldsHigh correlation
mean smoothness is highly correlated with mean compactness and 4 other fieldsHigh correlation
mean compactness is highly correlated with mean radius and 8 other fieldsHigh correlation
mean concavity is highly correlated with mean radius and 8 other fieldsHigh correlation
mean concave points is highly correlated with mean radius and 7 other fieldsHigh correlation
mean symmetry is highly correlated with mean smoothness and 4 other fieldsHigh correlation
mean fractal dimension is highly correlated with mean smoothness and 3 other fieldsHigh correlation
y is highly correlated with mean radius and 6 other fieldsHigh correlation
mean concavity has 13 (2.3%) zeros Zeros
mean concave points has 13 (2.3%) zeros Zeros

Reproduction

Analysis started2022-02-19 16:47:59.893261
Analysis finished2022-02-19 16:50:39.155062
Duration2 minutes and 39.26 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

mean radius
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct456
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.12729174
Minimum6.981
Maximum28.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-02-20T01:50:39.758614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.981
5-th percentile9.5292
Q111.7
median13.37
Q315.78
95-th percentile20.576
Maximum28.11
Range21.129
Interquartile range (IQR)4.08

Descriptive statistics

Standard deviation3.524048826
Coefficient of variation (CV)0.2494497099
Kurtosis0.8455216229
Mean14.12729174
Median Absolute Deviation (MAD)1.9
Skewness0.9423795717
Sum8038.429
Variance12.41892013
MonotonicityNot monotonic
2022-02-20T01:50:40.911182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.344
 
0.7%
11.713
 
0.5%
12.463
 
0.5%
13.053
 
0.5%
10.263
 
0.5%
13.853
 
0.5%
12.773
 
0.5%
13.173
 
0.5%
133
 
0.5%
15.463
 
0.5%
Other values (446)538
94.6%
ValueCountFrequency (%)
6.9811
0.2%
7.6911
0.2%
7.7291
0.2%
7.761
0.2%
8.1961
0.2%
8.2191
0.2%
8.5711
0.2%
8.5971
0.2%
8.5981
0.2%
8.6181
0.2%
ValueCountFrequency (%)
28.111
0.2%
27.421
0.2%
27.221
0.2%
25.731
0.2%
25.221
0.2%
24.631
0.2%
24.251
0.2%
23.511
0.2%
23.291
0.2%
23.271
0.2%

mean texture
Real number (ℝ≥0)

HIGH CORRELATION

Distinct479
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.28964851
Minimum9.71
Maximum39.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-02-20T01:50:41.777907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.71
5-th percentile13.088
Q116.17
median18.84
Q321.8
95-th percentile27.15
Maximum39.28
Range29.57
Interquartile range (IQR)5.63

Descriptive statistics

Standard deviation4.301035768
Coefficient of variation (CV)0.2229711841
Kurtosis0.7583189724
Mean19.28964851
Median Absolute Deviation (MAD)2.81
Skewness0.6504495421
Sum10975.81
Variance18.49890868
MonotonicityNot monotonic
2022-02-20T01:50:42.743085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.523
 
0.5%
16.853
 
0.5%
16.843
 
0.5%
19.833
 
0.5%
14.933
 
0.5%
17.463
 
0.5%
18.93
 
0.5%
15.73
 
0.5%
18.223
 
0.5%
20.222
 
0.4%
Other values (469)540
94.9%
ValueCountFrequency (%)
9.711
0.2%
10.381
0.2%
10.721
0.2%
10.821
0.2%
10.891
0.2%
10.911
0.2%
10.941
0.2%
11.281
0.2%
11.791
0.2%
11.891
0.2%
ValueCountFrequency (%)
39.281
0.2%
33.811
0.2%
33.561
0.2%
32.471
0.2%
31.121
0.2%
30.721
0.2%
30.621
0.2%
29.971
0.2%
29.811
0.2%
29.431
0.2%

mean perimeter
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct522
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.96903339
Minimum43.79
Maximum188.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-02-20T01:50:43.929254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum43.79
5-th percentile60.496
Q175.17
median86.24
Q3104.1
95-th percentile135.82
Maximum188.5
Range144.71
Interquartile range (IQR)28.93

Descriptive statistics

Standard deviation24.29898104
Coefficient of variation (CV)0.2642082899
Kurtosis0.9722135477
Mean91.96903339
Median Absolute Deviation (MAD)12.71
Skewness0.9906504254
Sum52330.38
Variance590.4404795
MonotonicityNot monotonic
2022-02-20T01:50:45.185869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.613
 
0.5%
87.763
 
0.5%
134.73
 
0.5%
93.972
 
0.4%
82.692
 
0.4%
120.22
 
0.4%
107.12
 
0.4%
79.192
 
0.4%
114.22
 
0.4%
58.792
 
0.4%
Other values (512)546
96.0%
ValueCountFrequency (%)
43.791
0.2%
47.921
0.2%
47.981
0.2%
48.341
0.2%
51.711
0.2%
53.271
0.2%
54.091
0.2%
54.341
0.2%
54.421
0.2%
54.531
0.2%
ValueCountFrequency (%)
188.51
0.2%
186.91
0.2%
182.11
0.2%
174.21
0.2%
171.51
0.2%
166.21
0.2%
165.51
0.2%
158.91
0.2%
155.11
0.2%
153.51
0.2%

mean area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct539
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean654.8891037
Minimum143.5
Maximum2501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-02-20T01:50:46.197811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum143.5
5-th percentile275.78
Q1420.3
median551.1
Q3782.7
95-th percentile1309.8
Maximum2501
Range2357.5
Interquartile range (IQR)362.4

Descriptive statistics

Standard deviation351.9141292
Coefficient of variation (CV)0.5373644594
Kurtosis3.652302762
Mean654.8891037
Median Absolute Deviation (MAD)153.3
Skewness1.645732176
Sum372631.9
Variance123843.5543
MonotonicityNot monotonic
2022-02-20T01:50:47.519377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
512.23
 
0.5%
10752
 
0.4%
582.72
 
0.4%
399.82
 
0.4%
641.22
 
0.4%
394.12
 
0.4%
372.72
 
0.4%
477.32
 
0.4%
758.62
 
0.4%
11382
 
0.4%
Other values (529)548
96.3%
ValueCountFrequency (%)
143.51
0.2%
170.41
0.2%
178.81
0.2%
1811
0.2%
201.91
0.2%
203.91
0.2%
221.21
0.2%
221.31
0.2%
221.81
0.2%
224.51
0.2%
ValueCountFrequency (%)
25011
0.2%
24991
0.2%
22501
0.2%
20101
0.2%
18781
0.2%
18411
0.2%
17611
0.2%
17471
0.2%
16861
0.2%
16851
0.2%

mean smoothness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct474
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0963602812
Minimum0.05263
Maximum0.1634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-02-20T01:50:48.444566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.05263
5-th percentile0.075042
Q10.08637
median0.09587
Q30.1053
95-th percentile0.11878
Maximum0.1634
Range0.11077
Interquartile range (IQR)0.01893

Descriptive statistics

Standard deviation0.01406412814
Coefficient of variation (CV)0.1459535813
Kurtosis0.8559749304
Mean0.0963602812
Median Absolute Deviation (MAD)0.0095
Skewness0.4563237648
Sum54.829
Variance0.0001977997003
MonotonicityNot monotonic
2022-02-20T01:50:49.947258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10075
 
0.9%
0.1154
 
0.7%
0.10544
 
0.7%
0.10754
 
0.7%
0.10633
 
0.5%
0.1173
 
0.5%
0.10493
 
0.5%
0.10443
 
0.5%
0.10663
 
0.5%
0.11583
 
0.5%
Other values (464)534
93.8%
ValueCountFrequency (%)
0.052631
0.2%
0.062511
0.2%
0.064291
0.2%
0.065761
0.2%
0.066131
0.2%
0.068281
0.2%
0.068831
0.2%
0.069351
0.2%
0.06951
0.2%
0.069551
0.2%
ValueCountFrequency (%)
0.16341
0.2%
0.14471
0.2%
0.14251
0.2%
0.13981
0.2%
0.13711
0.2%
0.13351
0.2%
0.13261
0.2%
0.13231
0.2%
0.12911
0.2%
0.12861
0.2%

mean compactness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct537
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1043409842
Minimum0.01938
Maximum0.3454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-02-20T01:50:50.907529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01938
5-th percentile0.04066
Q10.06492
median0.09263
Q30.1304
95-th percentile0.2087
Maximum0.3454
Range0.32602
Interquartile range (IQR)0.06548

Descriptive statistics

Standard deviation0.05281275793
Coefficient of variation (CV)0.5061554512
Kurtosis1.650130467
Mean0.1043409842
Median Absolute Deviation (MAD)0.03263
Skewness1.190123031
Sum59.37002
Variance0.0027891874
MonotonicityNot monotonic
2022-02-20T01:50:51.906833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11473
 
0.5%
0.12063
 
0.5%
0.076982
 
0.4%
0.057432
 
0.4%
0.038342
 
0.4%
0.15162
 
0.4%
0.11172
 
0.4%
0.11112
 
0.4%
0.20872
 
0.4%
0.10472
 
0.4%
Other values (527)547
96.1%
ValueCountFrequency (%)
0.019381
0.2%
0.023441
0.2%
0.02651
0.2%
0.026751
0.2%
0.031161
0.2%
0.032121
0.2%
0.033931
0.2%
0.033981
0.2%
0.034541
0.2%
0.035151
0.2%
ValueCountFrequency (%)
0.34541
0.2%
0.31141
0.2%
0.28671
0.2%
0.28391
0.2%
0.28321
0.2%
0.27761
0.2%
0.2771
0.2%
0.27681
0.2%
0.26651
0.2%
0.25761
0.2%

mean concavity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct537
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08879931582
Minimum0
Maximum0.4268
Zeros13
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-02-20T01:50:52.949896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0049826
Q10.02956
median0.06154
Q30.1307
95-th percentile0.24302
Maximum0.4268
Range0.4268
Interquartile range (IQR)0.10114

Descriptive statistics

Standard deviation0.07971980871
Coefficient of variation (CV)0.8977525105
Kurtosis1.998637529
Mean0.08879931582
Median Absolute Deviation (MAD)0.04046
Skewness1.401179739
Sum50.5268107
Variance0.0063552479
MonotonicityNot monotonic
2022-02-20T01:50:54.226418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013
 
2.3%
0.12043
 
0.5%
0.11152
 
0.4%
0.033442
 
0.4%
0.11032
 
0.4%
0.10852
 
0.4%
0.1012
 
0.4%
0.019722
 
0.4%
0.029952
 
0.4%
0.10072
 
0.4%
Other values (527)537
94.4%
ValueCountFrequency (%)
013
2.3%
0.0006921
 
0.2%
0.00097371
 
0.2%
0.0011941
 
0.2%
0.0014611
 
0.2%
0.0014871
 
0.2%
0.0015461
 
0.2%
0.0015951
 
0.2%
0.0015971
 
0.2%
0.001861
 
0.2%
ValueCountFrequency (%)
0.42681
0.2%
0.42641
0.2%
0.41081
0.2%
0.37541
0.2%
0.36351
0.2%
0.35231
0.2%
0.35141
0.2%
0.33681
0.2%
0.33391
0.2%
0.32011
0.2%

mean concave points
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct542
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04891914587
Minimum0
Maximum0.2012
Zeros13
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-02-20T01:50:55.554182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0056208
Q10.02031
median0.0335
Q30.074
95-th percentile0.12574
Maximum0.2012
Range0.2012
Interquartile range (IQR)0.05369

Descriptive statistics

Standard deviation0.03880284486
Coefficient of variation (CV)0.7932036459
Kurtosis1.066555703
Mean0.04891914587
Median Absolute Deviation (MAD)0.02014
Skewness1.171180081
Sum27.834994
Variance0.001505660769
MonotonicityNot monotonic
2022-02-20T01:50:57.662846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013
 
2.3%
0.028643
 
0.5%
0.14712
 
0.4%
0.057782
 
0.4%
0.022722
 
0.4%
0.023692
 
0.4%
0.023772
 
0.4%
0.025942
 
0.4%
0.052522
 
0.4%
0.020312
 
0.4%
Other values (532)537
94.4%
ValueCountFrequency (%)
013
2.3%
0.0018521
 
0.2%
0.0024041
 
0.2%
0.0029241
 
0.2%
0.0029411
 
0.2%
0.0031251
 
0.2%
0.0032611
 
0.2%
0.0033331
 
0.2%
0.0034721
 
0.2%
0.0041671
 
0.2%
ValueCountFrequency (%)
0.20121
0.2%
0.19131
0.2%
0.18781
0.2%
0.18451
0.2%
0.18231
0.2%
0.16891
0.2%
0.1621
0.2%
0.16041
0.2%
0.15951
0.2%
0.15621
0.2%

mean symmetry
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct432
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1811618629
Minimum0.106
Maximum0.304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-02-20T01:50:58.813912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.106
5-th percentile0.1415
Q10.1619
median0.1792
Q30.1957
95-th percentile0.23072
Maximum0.304
Range0.198
Interquartile range (IQR)0.0338

Descriptive statistics

Standard deviation0.02741428134
Coefficient of variation (CV)0.1513247926
Kurtosis1.287932992
Mean0.1811618629
Median Absolute Deviation (MAD)0.0171
Skewness0.7256089734
Sum103.0811
Variance0.0007515428212
MonotonicityNot monotonic
2022-02-20T01:51:00.273087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17144
 
0.7%
0.17694
 
0.7%
0.18934
 
0.7%
0.16014
 
0.7%
0.17174
 
0.7%
0.18613
 
0.5%
0.19663
 
0.5%
0.19253
 
0.5%
0.15063
 
0.5%
0.17393
 
0.5%
Other values (422)534
93.8%
ValueCountFrequency (%)
0.1061
0.2%
0.11671
0.2%
0.12031
0.2%
0.12151
0.2%
0.1221
0.2%
0.12741
0.2%
0.13051
0.2%
0.13081
0.2%
0.13371
0.2%
0.13391
0.2%
ValueCountFrequency (%)
0.3041
0.2%
0.29061
0.2%
0.27431
0.2%
0.26781
0.2%
0.26551
0.2%
0.25971
0.2%
0.25951
0.2%
0.25691
0.2%
0.25561
0.2%
0.25481
0.2%

mean fractal dimension
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct499
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06279760984
Minimum0.04996
Maximum0.09744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-02-20T01:51:01.433945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.04996
5-th percentile0.053926
Q10.0577
median0.06154
Q30.06612
95-th percentile0.07609
Maximum0.09744
Range0.04748
Interquartile range (IQR)0.00842

Descriptive statistics

Standard deviation0.007060362795
Coefficient of variation (CV)0.1124304382
Kurtosis3.00589212
Mean0.06279760984
Median Absolute Deviation (MAD)0.00422
Skewness1.304488813
Sum35.73184
Variance4.98487228 × 10-5
MonotonicityNot monotonic
2022-02-20T01:51:02.491410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.061133
 
0.5%
0.059133
 
0.5%
0.059073
 
0.5%
0.056673
 
0.5%
0.067823
 
0.5%
0.058662
 
0.4%
0.06022
 
0.4%
0.056742
 
0.4%
0.064122
 
0.4%
0.060192
 
0.4%
Other values (489)544
95.6%
ValueCountFrequency (%)
0.049961
0.2%
0.050241
0.2%
0.050251
0.2%
0.050441
0.2%
0.050541
0.2%
0.050961
0.2%
0.051761
0.2%
0.051771
0.2%
0.051851
0.2%
0.052231
0.2%
ValueCountFrequency (%)
0.097441
0.2%
0.095751
0.2%
0.095021
0.2%
0.092961
0.2%
0.08981
0.2%
0.087431
0.2%
0.08451
0.2%
0.082611
0.2%
0.082431
0.2%
0.081421
0.2%

y
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
1
357 
0
212 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1357
62.7%
0212
37.3%

Length

2022-02-20T01:51:03.439536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-20T01:51:04.211344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1357
62.7%
0212
37.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-02-20T01:50:28.470170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:48:51.285885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:03.323055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:14.263837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:26.311284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:37.572434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:48.969883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:59.691825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:08.772646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:19.246030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:29.262647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:48:52.495174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:04.261269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:15.330968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:27.210007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:38.388414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:49.809345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:00.651034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:09.864029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:20.306486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:30.006410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:48:53.738278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:05.547991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:16.712777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:28.908373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:39.371467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:50.669432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:01.687883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:10.876101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:21.301878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:30.777731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:48:54.986419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:06.746467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:17.985115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:30.278437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:40.473345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:51.803698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:02.736025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:11.981095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:22.299878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:31.561290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:48:56.089231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:07.688466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:18.987457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:31.237708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:41.512263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:52.637689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:03.898179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:13.311132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:23.085417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:32.425248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:48:57.325736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:08.725871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:20.117851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:32.416949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:42.491938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:53.517977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:04.743207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:14.629174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:24.010940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:33.269395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:48:58.841306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:09.782685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:21.465068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:33.493522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:43.514436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:54.651660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:05.491693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:15.556973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:25.047873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:34.027748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:48:59.789499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:10.626858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:22.584047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:34.350683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:45.283949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:55.981058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:06.162925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:16.659411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:25.950758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:34.957294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:01.340813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:11.672277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:23.780013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:35.452741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:46.831560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:57.620585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:06.921397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:17.542585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:26.800939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:35.771544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:02.212099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:13.116300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:25.268725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:36.627443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:47.857433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:49:58.879768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:07.814196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:18.427207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-20T01:50:27.651726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-02-20T01:51:04.836970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-20T01:51:06.188045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-20T01:51:07.523954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-20T01:51:08.897291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-20T01:50:37.070477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-20T01:50:38.524615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimensiony
017.9910.38122.801001.00.118400.277600.300100.147100.24190.078710
120.5717.77132.901326.00.084740.078640.086900.070170.18120.056670
219.6921.25130.001203.00.109600.159900.197400.127900.20690.059990
311.4220.3877.58386.10.142500.283900.241400.105200.25970.097440
420.2914.34135.101297.00.100300.132800.198000.104300.18090.058830
512.4515.7082.57477.10.127800.170000.157800.080890.20870.076130
618.2519.98119.601040.00.094630.109000.112700.074000.17940.057420
713.7120.8390.20577.90.118900.164500.093660.059850.21960.074510
813.0021.8287.50519.80.127300.193200.185900.093530.23500.073890
912.4624.0483.97475.90.118600.239600.227300.085430.20300.082430

Last rows

mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimensiony
55911.5123.9374.52403.50.092610.102100.111200.041050.13880.065701
56014.0527.1591.38600.40.099290.112600.044620.043040.15370.061711
56111.2029.3770.67386.00.074490.035580.000000.000000.10600.055021
56215.2230.62103.40716.90.104800.208700.255000.094290.21280.071520
56320.9225.09143.001347.00.109900.223600.317400.147400.21490.068790
56421.5622.39142.001479.00.111000.115900.243900.138900.17260.056230
56520.1328.25131.201261.00.097800.103400.144000.097910.17520.055330
56616.6028.08108.30858.10.084550.102300.092510.053020.15900.056480
56720.6029.33140.101265.00.117800.277000.351400.152000.23970.070160
5687.7624.5447.92181.00.052630.043620.000000.000000.15870.058841